Light transmission model for determination of the refractive index of solid particles in suspensions: an immersion study

We report on a model to determine the light transmission of solid particles in immersion liquids. Using the model, optical and physical properties of suspensions such as the refractive index of the particles can be retrieved. The model can also be used in the case of colored suspensions, where the detection of only the maximum transmission would give an inaccurate refractive index for a particle. The validity of the method was tested with CaF(2) particles. Potential applications include scientific studies of the optical properties of micro- and nanoparticles or microorganisms in a liquid matrix. The model is also useful in industry where monitoring of suspensions and colloids is an issue.     

Bilateral filtering of diffusion tensor magnetic resonance images.

We extend the well-known scalar image bilateral filtering technique to diffusion tensor magnetic resonance images (DTMRI). The scalar version of bilateral image filtering is extended to perform edge-preserving smoothing of DT field data. The bilateral DT filtering is performed in the Log-Euclidean framework which guarantees valid output tensors. Smoothing is achieved by weighted averaging of neighboring tensors. Analogous to bilateral filtering of scalar images, the weights are chosen to be inversely proportional to two distance measures: The geometrical Euclidean distance between the spatial locations of tensors and the dissimilarity of tensors. We describe the noniterative DT smoothing equation in closed form and show how interpolation of DT data is treated as a special case of bilateral filtering where only spatial distance is used. We evaluate different recent DT tensor dissimilarity metrics including the Log-Euclidean, the similarity-invariant Log-Euclidean, the square root of the J-divergence, and the distance scaled mutual diffusion coefficient. We present qualitative and quantitative smoothing and interpolation results and show their effect on segmentation, for both synthetic DT field data, as well as real cardiac and brain DTMRI data.     

Joint parameter estimation and symbol detection for linear ornonlinear unknown channels

We present an iterative method for joint channel parameter estimation and symbol selection via the Baum-Welch algorithm, or equivalently the Expectation-Maximization (EM) algorithm. Channel parameters, including noise variance, are estimated using a maximum likelihood criterion. The Markovian properties of the channel state sequence enable us to calculate the required likelihood using a forward-backward algorithm. The calculated likelihood functions can easily give optimum decisions on information symbols which minimize the symbol error probability. The proposed receiver can be used for both linear and nonlinear channels. It improves the system throughput by making saving in the transmission of known symbols, usually employed for channel identification. Simulation results which show fast convergence are presented     

Optimal packet length for wireless communications using reconfigurable intelligent surfaces

Telecommunication Systems - Reconfigurable intelligent surfaces (RIS) allow significant throughput enhancement as all reflections have the same phase at the receiver. In this paper, we suggest to...     

Hybrid RF/VLC Communications Using Reconfigurable Intelligent Surfaces

In this paper, we suggest the use of Reconfigurable Intelligent Surfaces (RIS) for hybrid Radio Frequency (RF) Visible Light Communications (VLC). The signal is transmitted from a transmitter T to a relay C using RIS. RIS is implemented as a transmitter or a reflector between T and C. The relay node C decodes the received signal and transmits it to the receiver R using a VLC link. The proposed VLC/RF communications using RIS offers 20–45 dB gain with respect to conventional RF/VLC when the number of reflectors N is varied from \(N=16\) to \(N=256\).

     

A solar and wind driven energy system for hydrogen and urea production with CO2 capturing

A novel solar PV and wind energy based system is proposed in this study for capturing carbon dioxide as well as producing hydrogen, urea and power. Both Aspen Plus and EES software packages are employed for analyses and simulations. The present system is designed in a way that PEM electrolyzer is powered by the wind turbines for hydrogen production, which is further converted into ammonia and then synthesizes urea by capturing CO₂ and additional power is supplied to the community. The solar PV is employed to power the cryogenic air separation unit and the additional power is used for the industrial purpose. In the proposed system, ammonia does not only capture CO₂ but also synthesizes urea for fertilizer industry. The amount of electrical power produced by the system is 2.14 MW. The designed system produces 518.4 kmol/d of hydrogen and synthesizes 86.4 kmol/d of urea. Furthermore, several parametric studies are employed to investigate the system performance.     

Alzheimer Disease Detection Empowered with Transfer Learning

Alzheimer's disease is a severe neuron disease that damages brain cells which leads to permanent loss of memory also called dementia. Many people die due to this disease every year because this is not curable but early detection of this disease can help restrain the spread. Alzheimer's is most common in elderly people in the age bracket of 65 and above. An automated system is required for early detection of disease that can detect and classify the disease into multiple Alzheimer classes. Deep learning and machine learning techniques are used to solve many medical problems like this. The proposed system Alzheimer Disease detection utilizes transfer learning on Multi-class classification using brain Medical resonance imagining (MRI) working to classify the images in four stages, Mild demented (MD), Moderate demented (MOD), Non-demented (ND), Very mild demented (VMD). Simulation results have shown that the proposed system model gives 91.70% accuracy. It also observed that the proposed system gives more accurate results as compared to previous approaches.     

Agent-Based Knowledge Analysis Framework in Disaster Management

Disaster Management (DM) is a complex set of interrelated activities. The activities are often knowledge-intensive and time sensitive. Timely sharing of the required knowledge is critical for DM. For recurring disasters (e.g. floods), developed countries tend to have dedicated document repositories of Disaster Management Plans (DISPLANs) that can be accessed as needs arise. However, accessing the appropriate plan in a timely manner, and sharing activities between plans, often requires significant domain knowledge and intimate understanding of the plans in the first place. This paper introduces an Agent-Based (AB) knowledge analysis method to convert DISPLANs into a collection of knowledge units that can be stored into a unified repository. The repository of DM actions then enables the mixing and matching of knowledge between different plans. The repository is structured as a layered abstraction according to Meta Object Facility (MOF). We use the flood DISPLANs plans used by SES (State Emergency Service), an authoritative DM agency in New South Wales (NSW) State of Australia (hereinafter referred to as SES NSW) to illustrate and give a preliminary validation of the approach. It is illustrated by using displans along the flood-prone Murrumbidgee river in central NSW.     

Evolving performance control systems for digital puppetry

We describe a new approach for creating performance control systems for digital puppetry. Genetic programming with fitness values specified directly by the puppeteer is used. A generic device and model representation combined with the inherent domain independence of the genetic programming paradigm allows this approach to create control systems for arbitrary combinations of input devices and models. In addition, a number of unique interaction techniques have been developed to support the user‐directed search. In this paper we introduce the approach, describe the implementation and user interface and present the results from a comprehensive evaluation with expert users. We show that a search‐based approach can provide an effective alternative to manually designing performance control systems and an elegant mechanism for unifying low‐level input devices with a broad range of model control modes. Copyright © 2000 John Wiley & Sons, Ltd.